The document provides an overview of the Seaborn Python library for statistical data visualization. It discusses preparing data, controlling figure aesthetics, basic plot types like scatter plots and histograms, customizing plots, and using built-in datasets. Key steps include importing libraries, setting the style, loading datasets, and calling plotting functions to visualize relationships in the data.

1. PythonForDataScience Cheat Sheet
Seaborn
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Statistical Data Visualization With Seaborn
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Learn Python for Data Science Interactively
Figure Aesthetics
Data
The Python visualization library Seaborn is based on
matplotlib and provides a high-level interface for drawing
attractive statistical graphics.
Make use of the following aliases to import the libraries:
The basic steps to creating plots with Seaborn are:
1. Prepare some data
2. Control figure aesthetics
3. Plot with Seaborn
4. Further customize your plot
>>> import pandas as pd
>>> import numpy as np
>>> uniform_data = np.random.rand(10, 12)
>>> data = pd.DataFrame({'x':np.arange(1,101),
'y':np.random.normal(0,4,100)})
>>> import matplotlib.pyplot as plt
>>> import seaborn as sns
Plotting With Seaborn
>>> import matplotlib.pyplot as plt
>>> import seaborn as sns
>>> tips = sns.load_dataset("tips")
>>> sns.set_style("whitegrid")
>>> g = sns.lmplot(x="tip",
y="total_bill",
data=tips,
aspect=2)
>>> g = (g.set_axis_labels("Tip","Total bill(USD)").
set(xlim=(0,10),ylim=(0,100)))
>>> plt.title("title")
>>> plt.show(g)
Step 4
Step 2
Step 1
Step 5
Step 3
1
>>> titanic = sns.load_dataset("titanic")
>>> iris = sns.load_dataset("iris")
Seaborn also offers built-in data sets:
2
3
Further Customizations4
Show or Save Plot
>>> sns.set() (Re)set the seaborn default
>>> sns.set_style("whitegrid") Set the matplotlib parameters
>>> sns.set_style("ticks", Set the matplotlib parameters
{"xtick.major.size":8,
"ytick.major.size":8})
>>> sns.axes_style("whitegrid") Return a dict of params or use with
with to temporarily set the style
Axis Grids
>>> f, ax = plt.subplots(figsize=(5,6)) Create a figure and one subplot
>>> plt.title("A Title") Add plot title
>>> plt.ylabel("Survived") Adjust the label of the y-axis
>>> plt.xlabel("Sex") Adjust the label of the x-axis
>>> plt.ylim(0,100) Adjust the limits of the y-axis
>>> plt.xlim(0,10) Adjust the limits of the x-axis
>>> plt.setp(ax,yticks=[0,5]) Adjust a plot property
>>> plt.tight_layout() Adjust subplot params
>>> plt.show() Show the plot
>>> plt.savefig("foo.png") Save the plot as a figure
>>> plt.savefig("foo.png", Save transparent figure
transparent=True)
>>> sns.regplot(x="sepal_width", Plot data and a linear regression
y="sepal_length", model fit
data=iris,
ax=ax)
>>> g.despine(left=True) Remove left spine
>>> g.set_ylabels("Survived") Set the labels of the y-axis
>>> g.set_xticklabels(rotation=45) Set the tick labels for x
>>> g.set_axis_labels("Survived", Set the axis labels
"Sex")
>>> h.set(xlim=(0,5), Set the limit and ticks of the
ylim=(0,5), x-and y-axis
xticks=[0,2.5,5],
yticks=[0,2.5,5])
Close & Clear
>>> plt.cla() Clear an axis
>>> plt.clf() Clear an entire figure
>>> plt.close() Close a window
5
Also see Lists, NumPy & Pandas
Also see Matplotlib
Also see Matplotlib
Also see Matplotlib
Also see Matplotlib
Context Functions
>>> sns.set_context("talk") Set context to "talk"
>>> sns.set_context("notebook", Set context to "notebook",
font_scale=1.5, Scale font elements and
rc={"lines.linewidth":2.5}) override param mapping
Seaborn styles
>>> sns.set_palette("husl",3) Define the color palette
>>> sns.color_palette("husl") Use with with to temporarily set palette
>>> flatui = ["#9b59b6","#3498db","#95a5a6","#e74c3c","#34495e","#2ecc71"]
>>> sns.set_palette(flatui) Set your own color palette
Color Palette
Plot
Axisgrid Objects
>>> g = sns.FacetGrid(titanic, Subplot grid for plotting conditional
col="survived", relationships
row="sex")
>>> g = g.map(plt.hist,"age")
>>> sns.factorplot(x="pclass", Draw a categorical plot onto a
y="survived", Facetgrid
hue="sex",
data=titanic)
>>> sns.lmplot(x="sepal_width", Plot data and regression model fits
y="sepal_length", across a FacetGrid
hue="species",
data=iris)
Regression PlotsCategorical Plots
Scatterplot
>>> sns.stripplot(x="species", Scatterplot with one
y="petal_length", categorical variable
data=iris)
>>> sns.swarmplot(x="species", Categorical scatterplot with
y="petal_length", non-overlapping points
data=iris)
Bar Chart
>>> sns.barplot(x="sex", Show point estimates and
y="survived", confidence intervals with
hue="class", scatterplot glyphs
data=titanic)
Count Plot
>>> sns.countplot(x="deck", Show count of observations
data=titanic,
palette="Greens_d")
Point Plot
>>> sns.pointplot(x="class", Show point estimates and
y="survived", confidence intervals as
hue="sex", rectangular bars
data=titanic,
palette={"male":"g",
"female":"m"},
markers=["^","o"],
linestyles=["-","--"])
Boxplot
>>> sns.boxplot(x="alive", Boxplot
y="age",
hue="adult_male",
data=titanic)
>>> sns.boxplot(data=iris,orient="h") Boxplot with wide-form data
Violinplot
>>> sns.violinplot(x="age", Violin plot
y="sex",
hue="survived",
data=titanic)
>>> plot = sns.distplot(data.y, Plot univariate distribution
kde=False,
color="b")
Distribution Plots
>>> h = sns.PairGrid(iris) Subplot grid for plotting pairwise
>>> h = h.map(plt.scatter) relationships
>>> sns.pairplot(iris) Plot pairwise bivariate distributions
>>> i = sns.JointGrid(x="x", Grid for bivariate plot with marginal
y="y", univariate plots
data=data)
>>> i = i.plot(sns.regplot,
sns.distplot)
>>> sns.jointplot("sepal_length", Plot bivariate distribution
"sepal_width",
data=iris,
kind='kde')
Matrix Plots
>>> sns.heatmap(uniform_data,vmin=0,vmax=1) Heatmap